# ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification

arXiv: https://arxiv.org/abs/2310.08036 · HTML: https://mlsanity.com/paper/2310.08036/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Feature Generating Networks for Zero-Shot Learning](https://mlsanity.com/paper/1712.00981/) (2017)
- [A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders](https://mlsanity.com/paper/1709.00663/) (2017)
- [IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT](https://mlsanity.com/paper/1611.04880/) (2016)
- [A Smart Home is No Castle: Privacy Vulnerabilities of Encrypted IoT Traffic](https://mlsanity.com/paper/1705.06805/) (2017)
- [IoTSense: Behavioral Fingerprinting of IoT Devices](https://mlsanity.com/paper/1804.03852/) (2018)
- [Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks](https://mlsanity.com/paper/1909.00104/) (2019)
- [Zero-shot Learning via Shared-Reconstruction-Graph Pursuit](https://mlsanity.com/paper/1711.07302/) (2017)
- [Cost-aware Feature Selection for IoT Device Classification](https://mlsanity.com/paper/2009.01368/) (2020)

## Built on by (most cited first, 3 of 3)

- [UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Security](https://mlsanity.com/paper/2503.04174/) (2025)
- [RECTor: Robust and Efficient Correlation Attack on Tor](https://mlsanity.com/paper/2512.00436/) (2025)
- [GETA: Generalized Encrypted Traffic Analysis](https://mlsanity.com/paper/2605.31277/) (2026)

## Abstract (arXiv)

Recent research works have proposed machine learning models for classifying IoT devices connected to a network. However, there is still a practical challenge of not having all devices (and hence their traffic) available during the training of a model. This essentially means, during the operational phase, we need to classify new devices not seen in the training phase. To address this challenge, we propose ZEST -- a ZSL (zero-shot learning) framework based on self-attention for classifying both seen and unseen devices. ZEST consists of i) a self-attention based network feature extractor, termed SANE, for extracting latent space representations of IoT traffic, ii) a generative model that trains a decoder using latent features to generate pseudo data, and iii) a supervised model that is trained on the generated pseudo data for classifying devices. We carry out extensive experiments on real IoT traffic data; our experiments demonstrate i) ZEST achieves significant improvement (in terms of accuracy) over the baselines; ii) SANE is able to better extract meaningful representations than LSTM which has been commonly used for modeling network traffic.